Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Association rules’learning is a machine learning method used in finding underlying associations in large datasets.Whether intentionally or unintentionally present,noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy.This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier.Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning.This paper utilizes them to remove noise from the dataset before training the association rules classifier.Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques,namely:DecrementalReduction Optimization Procedure(DROP)3,DROP5,ALL K-Nearest Neighbors(ALLKNN),Edited Nearest Neighbor(ENN),and Repeated Edited Nearest Neighbor(RENN)in different noise ratios.Experiments show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels:0%,5%,and 10%.The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine(UCI)machine learning repository.The improvements were more apparent in the 5%and the 10%noise cases.When RENN was applied,the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47%to 76.65%compared to the original test.The average accuracy was improved from 66.08%to 77.47%for the 5%-noise case and from 59.89%to 77.59%in the 10%-noise case.Higher confidence was also reported in building the association rules when RENN was used.The above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier,especially in noisy domains.

Rule of prescribing traditional Chinese medicine in the treatment of pneumoconiosis basedon association rules and k-means clustering algorithm

Objective:To analyze the rule of prescribing traditional Chinese medicine for treating pneumoconiosis,so as to provide reference for differential diagnosis and treatment of pneumoconiosis as well as for the development of new drugs for treatingthe disease.Methods:We searched China National Knowledge Infrastructure,Wanfang Database and VIP Chinese PublicationDatabase to retrieve relevant literatures which were then screened according to the enrollment criteria to establish a prescriptiondatabase of traditional Chinese medicine for the treatment of pneumoconiosis.The inheritance calculation platform of traditionalChinese medicine was used to analyze the prescribing rule of traditional Chinese medicine in the treatment of pneumoconiosisbased on association rules,k-means clustering algorithm and regression model analysis.Results:A total of 131 related literature were preliminarily selected,from which 97 prescriptions of traditional Chinese medicine with a total of 195 herbs were included.The most frequently prescribed herbs included Radix astragali,Platycodon grandiflorum,Pinellia ternata,licorice,Codonopsispilosula,Salvia miltiorrhiza,bitter almond etc.A total of 14 association rules,13 high-frequency herb pairs were found and 5groups of formulas were revealed by cluster analysis.Conclusion:The prescriptions for the treatment of pneumoconiosis are mainly composed of herbs for tonifying deficiency,resolving phlegm,relieving cough and asthma,activating blood circulation and removingblood stasis,which are supplemented with herbs for clearing heat,relieving appearance,regulating qi,promoting waterand permeating dampness,etc.,The prescribing rules reflect the basic pathological characteristics of lung deficiency and collateral arthralgia in pneumoconiosis,which provides some ideas for the clinical differentiation and treatment of pneumoconiosis in traditionalChinese medicine.It also provides reference for the research and development of new treatment methods.

Compatibility Rules of Neonatal Parenteral Nutrition Prescriptions Based on Association Rules and Hierarchical Cluster Analysis

[Objectives]To explore the compatibility rules of neonatal parenteral nutrition(PN)prescriptions based on association rules and hierarchical cluster analysis,thereby providing a reference for standardizing neonatal parenteral nutrition supportive therapy.[Methods]The data about neonatal PN formulations prepared by the Pharmacy Intravenous Admixture Services(PIVAS)of the Affiliated Hospital of Chengde Medical University from July 2015 to June 2021 were collected.The general information of the prescriptions and the frequency of drug use were analyzed with Excel 2019;the boxplot of drug dosing was drawn using GraphPad 8.0 software;and SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to perform association rules and hierarchical cluster analysis.[Results]A total of 11488 PN prescriptions were collected from 1421 newborns,involving 18 kinds of drugs,which were divided into 11 types of nutrients.Association rules analysis yielded 84 nutrient substance combinations.The combination of fat emulsion-water-soluble vitamins-fat-soluble vitamins-glucose-amino acids had the highest confidence(99.95%).The hierarchical cluster analysis divided nutrients into 5 types.[Conclusions]The prescriptions of PN for newborns were composed of five types of nutrients:amino acids,fat emulsion,glucose,water-soluble vitamins,and fat-soluble vitamins.According to the lack of electrolytes and trace elements,appropriate drugs can be chosen to meet nutritional demands.This study provides reference basis for reasonable selection of drugs for neonatal PN prescriptions and further standardization of PN supportive therapy in newborns.

Ethics Lines and Machine Learning: A Design and Simulation of an Association Rules Algorithm for Exploiting the Data

Data mining techniques offer great opportunities for developing ethics lines whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate.

AN EVALUATION APPROACH FOR THE PROGRAM OF ASSOCIATION RULES ALGORITHM BASED ON METAMORPHIC RELATIONS

As data mining more and more popular applied in computer system,the quality as-surance test of its software would be get more and more attention.However,because of the ex-istence of the 'oracle' problem,the traditional test method is not ease fit for the application program in the field of the data mining.In this paper,based on metamorphic testing,a software testing method is proposed in the field of the data mining,makes an association rules algorithm as the specific case,and constructs the metamorphic relation on the algorithm.Experiences show that the method can achieve the testing target and is feasible to apply to other domain.

Elicitation of Association Rules from Information on Customs Offences on the Basis of Frequent Motives

The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of its implementation. As part of the customs clearance process, customs are nowadays confronted with an increasing volume of goods in connection with the development of international trade. Automated risk management is therefore required to limit intrusive control. In this article, we propose an unsupervised classification method to extract knowledge rules from a database of customs offences in order to identify abnormal behaviour resulting from customs control. The idea is to apply the Apriori principle on the basis of frequent grounds on a database relating to customs offences in customs procedures to uncover potential rules of association between a customs operation and an offence for the purpose of extracting knowledge governing the occurrence of fraud. This mass of often heterogeneous and complex data thus generates new needs that knowledge extraction methods must be able to meet. The assessment of infringements inevitably requires a proper identification of the risks. It is an original approach based on data mining or data mining to build association rules in two steps: first, search for frequent patterns (support >= minimum support) then from the frequent patterns, produce association rules (Trust >= Minimum Trust). The simulations carried out highlighted three main association rules: forecasting rules, targeting rules and neutral rules with the introduction of a third indicator of rule relevance which is the Lift measure. Confidence in the first two rules has been set at least 50%.

Discovering hidden patterns:Association rules for cardiovascular diseases in type 2 diabetes mellitus

BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Discovering Association Rules with Graph Patterns in Temporal Networks

Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal graph association rules(TGARs)that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints.We introduce quality measures(e.g.,support,confidence,and diversification)to characterize meaningful TGARs that are useful and diversified.In addition,the proposed support metric is an upper bound for alternative metrics,allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs.The diversification score strikes a balance between interestingness and diversity.Although the problem is NP-hard,we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph.We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible.Moreover,we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space.We experimentally verify the effectiveness,efficiency,and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.

Analysis of HAZMAT truck driver fatigue and distracted driving with warning-based data and association rules mining

Professional drivers are more frequently exposed to longer driving distance and travel time,leading to a higher possibility of safety risk for distraction and fatigue.The widespread and common use of commercial driver monitoring systems(DMS)provides a potential for data collection.It increases the amount of data characterizing driver behavior that can be used for further safety research.This study utilized DMS warning-based data and applied an association rule mining approach to explore risk factors contributing to hazardous materials(HAZMAT)truck driver inattention.A total of 499 HAZMAT truck driver inattentive warning events were used to find rules that will predict the occurrence of driver’s fatigue and distraction.First,Fisher’s exact tests were performed to examine the association between the frequency of driver inattentive behavior warnings and risk factors.Second,support,confidence,and lift values were used as measurements to quantify the relative strength of the association rules generated by the Apriori algorithm.Results show that speed between 40and 49 km/h,relatively longer travel time(3-6 h),freeway,tangent section,off-peak hour and clear weather condition are found to be highly associated with fatigue driving,while nighttime during 18:00 to 23:59,speed between 70 and 80 km/h,travel time between 1 and 3 h,freeways,acceleration less than 0.5 m/s^(2),visibility greater than 1000 m,and tangent roadway section are found to be highly associated with distracted driving.By focusing on the specific feature groups,these association rules would help in the development of mitigating distraction and fatigue driving countermeasures and enforcement approaches.

Mining item-item and between-set correlated association rules

To overcome the failure in eliminating suspicious patterns or association rules existing in traditional association rules mining, we propose a novel method to mine item-item and between-set correlated association rules. First, we present three measurements: the association, correlation, and item-set correlation measurements. In the association measurement, the all-confidence measure is used to filter suspicious cross-support patterns, while the all-item-confidence measure is applied in the correlation measurement to eliminate spurious association rules that contain negatively correlated items. Then, we define the item-set correlation measurement and show its corresponding properties. By using this measurement, spurious association rules in which the antecedent and consequent item-sets are negatively correlated can be eliminated. Finally, we propose item-item and between-set correlated association rules and two mining algorithms, I&ISCoMine_AP and I&ISCoMine_CT. Experimental results with synthetic and real retail datasets show that the proposed method is effective and valid.

Parallel mining and application of fuzzy association rules

Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means algorithm.Fuzzy c-means algorithm can embody the actual distribution of the data,and fuzzy sets can soften the partition boundary.Then,we improve the search technology of apriori algorithm and present the algorithm for mining fuzzy association rules.As the database size becomes larger and larger,a better way is to mine fuzzy association rules in parallel.In the parallel mining algorithm,quantitative attributes are partitioned into several fuzzy sets by using parallel fuzzy c-means algorithm.Boolean parallel algorithm is improved to discover frequent fuzzy attribute set,and the fuzzy association rules with at least a minimum confidence are generated on all processors.The experiment results implemented on the distributed linked PC/workstation show that the parallel mining algorithm has fine scaleup,sizeup and speedup.Last,we discuss the application of fuzzy association rules in the classification.The example shows that the accuracy of classification systems of the fuzzy association rules is better than that of the two popular classification methods:C4.5 and CBA.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Analysis of the medication rules of the national patent compound for the treatment of functional dyspepsia

Objective:Based on data mining software,applying frequent itemsets,association rules,hierarchical clustering,complex networks and other data mining methods to analyze the usage and compatibility of traditional Chinese medicine(TCM)patent compound for functional dyspepsia.Method:Use the Chinese patent database to search the compound for the treatment of functional dyspepsia,exclude traditional Chinese medicine extracts,single drugs,combined use of Chinese and Western medicines,etc.,screen the patented compound of TCM,establish an Excel data table,and apply data mining software to The data is subjected to frequency statistics,association rules,cluster analysis and complex network analysis.Result:A total of 238 prescriptions for functional dyspepsia were screened.The four qi of the drugs were mainly warm and calm,the five flavors were mainly sweet and spicy,and the spleen and stomach were the main meridians.The top 10 Chinese medicines with higher frequency are Shanzha、Chenpi、Gancao、Maiya、Jineijin、Fuling、Baizhu、Shenqu、Houpo、Banxia;frequent itemsets show that the drugs are mainly compatible with qi and spleen,qi and digestion;association rules The analysis shows that the common drug pairs used in the treatment of functional dyspepsia include Chenpi-Shanzha、Maiya-Shanzha、Jineijin-Shanzha,etc.;cluster analysis found that there are 4 types of drugs for functional dyspepsia,mainly including drugs for regulating qi-flowing for harmonizing stomach,drugs for soothing liver and promoting Qi,drugs for eliminating food and resolving accumulation,drugs for benefiting qi and strengthening spleen;the 22-flavor Chinese medicine in the core drug network,the core compatibility is mainly to eliminate stagnation and spleen.Conclusion:Data mining research provides a reference for the clinical treatment of functional dyspepsia and the development of TCM formulas;Clinical treatment of functional dyspepsia should grasp the basic principles of strengthening vital energy and eliminating pathogenic factors to benefit qi,strengthen the spleen,and eliminate food.It is a basic treatment method,taking into account the methods of regulating qi-flowing for harmonizing stomach,soothing the liver and relieving depression,relieving dampness and dampness,and combining the specific conditions of patients with syndrome differentiation and treatment.

CARM:Context Based Association Rule Mining for Conventional Data

This paper is aimed to develop an algorithm for extracting association rules,called Context-Based Association Rule Mining algorithm(CARM),which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm(CBPNARM).CBPNARM was developed to extract positive and negative association rules from Spatiotemporal(space-time)data only,while the proposed algorithm can be applied to both spatial and non-spatial data.The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations.Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique.The context,in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data.Conditional Probability Increment Ratio(CPIR)is also added in the proposed algorithm that was not used in CBPNARM.The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use.Also,the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM.The rules created by the proposed algorithm are more meaningful,significant,relevant and insightful.The accuracy of the proposed algorithm is compared with the Apriori,PNARM and CBPNARM algorithms.The results demonstrated enhanced accuracy,relevance and timeliness.

Unlocking new potential of clinical diagnosis with artificial intelligence:Finding new patterns of clinical and lab data

Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)mortality.CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle choices.AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages.Personalized medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome.These technological advancements enhance the computational analyses for both research and clinical practice.ARM plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor identification.AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy concerns.Thus,an AI ethics framework is essential to guide its responsible use.Aligning AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.

The analysis of acupoint selection rules for acupuncture treating functional constipation

Objective: To analyze the prescription rule of acupuncture for functional constipation (FC), and explore the effective core clinical acupuncture prescriptions. Methods:The randomized controlled trials published in the PubMed, CNKI, Wanfang, and VIP databases were retrieved from January 1, 2010 to January 31, 2020. And the key information about acupuncture therapy for FC in these RCTs was collected according to the predetermined inclusion and exclusion criteria. Eventually, the statisticians analyzed the use frequency of single acupoints and meridian acupoints by descriptive statistical method and explored the combination rule between different acupoints by association rules. Results: Twenty six randomized controlled trials were included, with a total of 29 acupoints. Tianshu (ST25), Shangjuxu (ST37) and Zhongwan (RN12) were the top 3 frequency of acupuncture therapy for FC and the top 3 frequency meridian had been chosen were the stomach meridian of Foot-Yangming, the spleen meridian of Foot-Taiyin and Ren channel. The two acupoints with high support and compatibility frequency ranking in the top 3 are Tianshu (ST25) - Shangjuxu (ST37), Tianshu (ST25) - Zhongwan (RN12), Tianshu (ST25) - Zusanli (ST36). The three acupoints with compatibility frequency ranking in the top 2 are Tianshu (ST25) - Shangjuxu (ST37) - Zusanli (ST36), and Tianshu (ST25) - Shangjuxu (ST37) - Zhigou (SJ6). Conclusion: Tianshu (ST25) is the most common acupoint for acupuncture therapy of FC. Tianshu (ST25) and Shangjuxu (ST37) are the core acupoints to provide basis for clinical practice and future clinical research.

Alarm Correlation Rules Generation Algorithm Based on Confidence Covered Value

In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm based on the confidence covered value.Confidence covered value method can judge whether a rule is redundant or not scientific After the rules that based on weighted frequent patterns(WFPs) generated,the association rules were deleted by the confidence covered value,in order to delete the redundant rules and keep the rules with more information.Experiments show that the alarm correlation rules generation algorithm based on the confidence covered value has higher efficiency than the traditional method,and can effectively remove redundant rules.Thus it is very suitable for telecommunication alarm association rules processing.

Acupoint selection rules for acupoint application in lung diseases in the post-epidemic era

Objective:To summarize the rules of acupoint selection of acupoint application to prevent and treat lung diseases under the background the post-epidemic era using data-mining technology.Method:The CNKI,Wanfang database,and VIP database were searched for clinical study articles on lung diseases treated by acupoint application published in the past 5 years.Data-eligible papers were extracted to establish a database of acupoint application for lung disease using Microsoft Excel 2019,with the goal of analyzing the frequency of acupoints,acupoint-meridian association,acupoint-location association,specific acupoint frequency,and cluster analysis.Association rules,consisting of acupoints with an application frequency of≥10,were devised by the Apriori algorithm to explore the correlation between acupoint groups and to analyze the rules of the compatibility of acupoint prescriptions.Results:A total of 229 eligible papers met our inclusion criteria.Forty-seven acupoints were applied,for a total frequency of acupoints of 1,035 times.Among these,acupoints for lung diseases were primarily distributed in the back-and-waist and chest-and-abdomen areas.From the analysis of the association rules,we obtained four groups of acupoint association rules based on acupoint clusters with a frequency≥10 and found that Feishu(BL 13),Tiantu(CV 22),Dazhui(GV 14),Dingchuan(EX-B1),and Danzhong(CV 17)constitute the core acupoints of prescriptions for clinical acupoint application to prevent and treat lung diseases.Conclusion:It is clearly shown that the core acupoints are relatively concentrated and that the selected acupoints were mainly locally selected,which could be a matching reference for the long-term prevention and treatment of lung diseases,including COVID-19.

Evaluation of Factors Affecting Driver’s Behaviors Using Association Rule

In this paper,association rule mining algorithm is utilized to analyze the correlations of various factors of causing traffic accidents,from which the relationship model of dangerous driving behaviors is established.In this model,the factors and their correlations include:ability of risk control,ability of driving self-confidence,individual characteristics,and incorrect driving operations.By selecting the drivers in the city of Chengdu to be the objects of investigation,a group of valid sample data is obtained.Based on these data,the Support and Confidence for association rules are analyzed.In the analysis,the two stage computing of Apriori algorithm programming is simulated,and from which some important rules are obtained.With these rules,departments of traffic administration can focus on these key factors in their processing of traffic transactions.By the training of drivers’skills and their physical and mental behaviors,the incorrect driving operations can be greatly reduced and the traffic safety can be effectively guaranteed.